CN115830874A - Method and system for evaluating fitting performance of traffic flow basic diagram - Google Patents
Method and system for evaluating fitting performance of traffic flow basic diagram Download PDFInfo
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Abstract
The invention provides a method and a system for evaluating the fitting performance of a traffic flow basic diagram, which relate to the technical field of model evaluation and comprise the steps of obtaining the vehicle count and the time average vehicle speed of a traffic flow in a preset period; dividing the preset period into a plurality of time intervals, and preprocessing the vehicle count and the time average speed to obtain a first average vehicle speed and a first average density corresponding to the traffic flow in each time interval; obtaining a traffic flow basic diagram to be evaluated, and constructing a corresponding linear converter based on a model of the traffic flow basic diagram; performing coordinate transformation on the first average vehicle speed by using the linear converter; calculating a weighting decision coefficient of the traffic flow in the preset period; according to the weighting decision coefficient, the fitting performance of the traffic flow basic diagram to the traffic flow is evaluated, and the invention provides a quantitative method for evaluating the fitting performance of the traffic flow basic diagram to empirical data under different traffic flow densities.
Description
Technical Field
The invention relates to the technical field of model evaluation, in particular to a method and a system for evaluating the fitting performance of a traffic flow basic graph.
Background
The basic map of traffic flow describes the functional relationship between macroscopic traffic flow parameters (flow, density and spatial average speed), which is crucial for traffic operation and management. The existing evaluation methods for traffic flow basic diagrams only can evaluate the existing basic diagrams and cannot further guide the optimization of the basic diagrams according to empirical data; the index of statistical analysis may be used as an optimization target for parameter calibration of a specific basic graph model, but if the basic graph model itself cannot accurately represent empirical samples, such a statistical analysis index cannot better describe empirical data beyond the upper limit of the basic graph model. Therefore, there is no effective quantitative method for evaluating the fitting performance of the traffic flow basic diagram to the empirical data under different traffic flow densities.
Disclosure of Invention
The invention aims to provide a method and a system for evaluating the fitting performance of a traffic flow basic diagram so as to solve the problems. In order to achieve the purpose, the technical scheme adopted by the invention is as follows:
in a first aspect, the present application provides a method for evaluating the fitting performance of a traffic flow basic diagram, including:
acquiring vehicle count and time average speed of a traffic flow in a preset period;
dividing the preset period into a plurality of time intervals, and preprocessing the vehicle count and the time average speed to obtain a first average vehicle speed and a first average density corresponding to the traffic flow in each time interval;
obtaining a traffic flow basic diagram to be evaluated, and constructing a corresponding linear converter based on a model of the traffic flow basic diagram;
performing coordinate transformation on the first average vehicle speed by using the linear converter;
calculating to obtain a weighting decision coefficient of the traffic flow in the preset period based on the density of all the time periods and the first average vehicle speed after coordinate conversion;
and evaluating the fitting performance of the traffic flow basic diagram to the traffic flow according to the weighting decision coefficient.
In a second aspect, the present application also provides a fitting performance evaluation system for a traffic flow basic diagram, including:
a first obtaining module: acquiring vehicle count and time average speed of a traffic flow in a preset period;
a processing module: dividing the preset period into a plurality of time intervals, and preprocessing the vehicle count and the time average speed to obtain a first average vehicle speed and a first average density corresponding to the traffic flow in each time interval;
a second obtaining module: acquiring a traffic flow basic diagram to be evaluated, and constructing a corresponding linear converter based on a model of the traffic flow basic diagram;
a coordinate conversion module: performing coordinate transformation on the first average vehicle speed by using the linear converter;
a calculation module: calculating to obtain a weighting decision coefficient of the traffic flow in the preset period based on the density of all the time periods and the first average vehicle speed after coordinate conversion;
an evaluation module: and evaluating the fitting performance of the traffic flow basic diagram to the traffic flow according to the weighting decision coefficient.
In a third aspect, the present application also provides a fitting performance evaluation apparatus for a traffic flow basic diagram, including:
a memory for storing a computer program;
and the processor is used for realizing the steps of the method for evaluating the fitting performance of the traffic flow basic diagram when executing the computer program.
In a fourth aspect, the present application further provides a readable storage medium, where a computer program is stored, and when the computer program is executed by a processor, the steps of the fitting performance evaluation method based on the traffic flow basic diagram are implemented.
The invention has the beneficial effects that:
the invention constructs a linear converter for the traffic flow basic diagram, and linearly converts the empirical data to make the basic diagram linear, and if the basic diagram to be evaluated can be better matched with the empirical data, the converted data can also be in an obvious linear trend, which indicates that the fitting performance of the traffic flow basic diagram is better. Therefore, a weighted decision coefficient of the traffic flow is calculated according to the converted empirical data and is used for evaluating the linear trend of the converted empirical data, so that the fitting performance of the traffic flow basic diagram is quantitatively evaluated.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by the practice of the embodiments of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained according to the drawings without inventive efforts.
FIG. 1 is a schematic flow chart of a method for evaluating the fitting performance of a basic map of a traffic flow according to an embodiment of the present invention;
FIG. 2 is a schematic structural diagram of a fitting performance evaluation system of a traffic flow basic diagram according to an embodiment of the present invention;
fig. 3 is a schematic structural view of a fitting performance evaluation device of a traffic flow basic diagram according to an embodiment of the present invention.
The labels in the figure are:
01. a first acquisition module; 02. a processing module; 021. a first calculation unit; 022. a second calculation unit; 023. a third calculation unit; 024. a judgment unit; 03. a second acquisition module; 031. a first acquisition unit; 032. a construction unit; 04. a coordinate conversion module; 05. a calculation module; 051. a sorting unit; 052. a fourth calculation unit; 053. a fifth calculation unit; 054. a sixth calculation unit; 06. an evaluation module;
800. a fitting performance evaluation device of the traffic flow basic diagram; 801. a processor; 802. a memory; 803. a multimedia component; 804. an I/O interface; 805. a communication component.
Detailed description of the preferred embodiments
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. The components of embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the present invention, presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, it need not be further defined or explained in subsequent figures. Meanwhile, in the description of the present invention, the terms "first", "second", and the like are used only for distinguishing the description, and are not to be construed as indicating or implying relative importance.
Example 1
The embodiment provides a method for evaluating the fitting performance of a traffic flow basic diagram.
Referring to fig. 1, the method is shown to include:
s1, obtaining vehicle count and time average speed of a traffic flow in a preset period;
in this embodiment, the preset period may be 1 month or 2 months, and the vehicle count and the time-average vehicle speed are collected by the coil detector in a 30s set.
Based on the above embodiment, the method further includes:
s2, dividing the preset period into a plurality of time intervals, and preprocessing the vehicle count and the time average speed to obtain a first average vehicle speed and a first average density corresponding to the traffic flow in each time interval;
in this embodiment, the preset interval is divided into time periods with a duration of T, so as to obtain n time periods, where T is an integral multiple of 30, and preferably, T =300s.
Specifically, the step S2 includes:
s21, calculating to obtain the flow rate corresponding to the traffic flow in each time interval according to the vehicle count;
s22, calculating according to the time average speed to obtain a first average speed corresponding to the traffic flow in each time interval;
where q represents the flow rate (hourly traffic volume), i represents the ith 30s during T period,
represents the ith 30s vehicle count over the T period; v represents a first average velocity over a period T,
represents the time-average vehicle speed of the ith 30s within the T period,
representing the variance of the average speed over time over the T period.
S23, calculating to obtain the density corresponding to the traffic flow in each time interval by using the first average speed and the flow rate corresponding to the traffic flow in each time interval;
where k is the density of the T period.
S24, judging whether an unreasonable first average vehicle speed, flow rate or density exists in each time interval according to preset judgment conditions:
specifically, the preset determination condition includes:
judging whether the first average vehicle speed, the flow rate or the density corresponding to any one time period is smaller than a first preset value: if so, indicating that the data in the time interval is unreasonable;
otherwise, continuously judging whether the flow rate is greater than the first preset value while the first average vehicle speed is the first preset value:
if so, indicating that the data in the time interval is unreasonable; otherwise, continuously judging whether the first average vehicle speed is greater than a second preset value:
if so, indicating that the data in the time interval is unreasonable;
otherwise, the data representing the time period is reasonable.
In this embodiment, the first preset value is set to be 0, the second preset value is set to be 350km/h, that is, the preset determination condition is:
1) q, k or
One term present is less than 0;
2）
=0 and q>0；
3）
>350km/h；
If one of the above 3 conditions is met, it indicates that the data in the T period is not reasonable.
If so, rejecting data corresponding to time periods in which an unreasonable first average vehicle speed, flow rate, or density exists.
And taking the traffic flow data obtained after the unreasonable data is removed as empirical data.
Based on the above embodiment, the method further includes:
s3, obtaining a traffic flow basic diagram to be evaluated, and constructing a corresponding linear converter based on a model of the traffic flow basic diagram;
specifically, the step S3 includes:
s31, acquiring a traffic flow basic diagram to be evaluated and a general formula of a linear converter;
the traffic flow basic map models include single-segment traffic flow basic map models (such as greenshiels, underwood, drake, and Newell), multi-segment traffic flow basic map models (such as dagazo and Smulders), and random traffic flow basic map models considering driver heterogeneity. Each traffic flow basic map contains a series of parameters which are calibrated primarily through empirical data or determined directly from traffic scenarios. Different traffic flow basic graph models or parameter calibration methods will produce different traffic flow functional relationships.
In this embodiment, a Drake traffic flow basic diagram is used as a traffic flow basic diagram to be evaluated, and a model of the Drake traffic flow basic diagram is as follows:
in the formula (I), the compound is shown in the specification,
representing an estimate of velocity of the base map as a function of density, k representing density,
and b both represent parameters.
The general formula of the linear converter is shown as follows;
in the formula (I), the compound is shown in the specification,
a linear converter is shown in which the output of the converter,
representing the maximum density observed from the historical data,
to represent
The inverse function of (a);
s32, combining the general formula of the linear converter with a model of a Drake traffic flow basic diagram to obtain the linear converter corresponding to the traffic flow basic diagram:
obtained according to equations (4) and (5):
based on the above embodiment, the method further comprises:
s4, carrying out coordinate transformation on the first average vehicle speed by using the linear converter;
specifically, a first average vehicle speed in the empirical data is obtained, and coordinate transformation is performed on the first average vehicle speed by using equation (6), so as to obtain:
in the formula (I), the compound is shown in the specification,
the first average vehicle speed coordinate converted value is represented.
Based on the above embodiment, the method further includes:
s5, calculating to obtain a weighting decision coefficient of the traffic flow in the preset period based on the density of all the time periods and the first average speed after coordinate conversion;
specifically, the step S5 includes:
s51, corresponding all time intervalsAre arranged in an ascending order of density,
wherein k is (1) Denotes the minimum value of density, k (n) Denotes the maximum value of density, k (2) Indicates the density, k, ranked second (j) Ranked as the jth density.
S52, calculating according to the sorted density to obtain the weight corresponding to each time interval;
specifically, whether the density of the current time period is an extreme value of the traffic flow is judged:
if not, calculating to obtain the weight of the current time interval according to the density of the current time interval and the density of the last time interval of the current time interval, wherein the calculation method comprises the following steps:
If yes, judging the density of the current time interval as the highest value or the lowest value of the traffic flow:
if it is the lowest value, then
；
If it is the highest value, then
。
S53, calculating according to the first average vehicle speed after coordinate conversion to obtain a first average vehicle speed average value after coordinate conversion
；
And S54, calculating the weighted decision coefficient of the traffic flow according to the density corresponding to all the time intervals, the first average speed after coordinate conversion, the first average speed average value and the weight.
In the formula (I), the compound is shown in the specification,
the weight-determining coefficient is represented by a weight,
and the first average speed after coordinate conversion corresponding to the m-th time period is sorted.
Based on the above embodiment, the method further includes:
s6, evaluating the fitting performance of the traffic flow basic diagram to the traffic flow according to the weighting decision coefficient;
in particular, the method comprises the following steps of,
the closer to 1, the better the fitting performance of the traffic flow basic diagram.
Example 2:
as shown in fig. 2, the present embodiment provides a fitting performance evaluation system of a traffic flow basic diagram, the system including:
the method comprises the steps of obtaining vehicle count and time average vehicle speed of a traffic flow in a preset period;
the processing module 02 divides the preset period into a plurality of time periods, and carries out preprocessing on the vehicle count and the time average speed to obtain a first average vehicle speed and density corresponding to the traffic flow in each time period;
a second obtaining module 03 is used for obtaining a traffic flow basic diagram to be evaluated and constructing a linear converter corresponding to the traffic flow basic diagram;
the coordinate conversion module 04 is used for carrying out coordinate conversion on the first average vehicle speed by utilizing the linear converter;
the calculation module 05 is used for calculating a weighting decision coefficient of the traffic flow in the preset period based on the density of all the time periods and the first average vehicle speed after coordinate conversion;
and the evaluation module 06 evaluates the fitting performance of the traffic flow basic diagram to the traffic flow according to the weighting decision coefficient.
Based on the above embodiment, the processing module 02 includes:
the first calculation unit 021 is used for calculating and obtaining the flow rate corresponding to the traffic flow in each time interval according to the vehicle count;
second calculation unit 022: calculating to obtain a first average speed corresponding to the traffic flow in each time interval according to the time average speed;
the third calculation unit 023: calculating to obtain the density corresponding to the traffic flow in each time period by using the first average vehicle speed and the flow rate corresponding to the traffic flow in each time period;
judging unit 024, judging whether unreasonable first average vehicle speed, flow rate or density exists in each time interval according to preset judging conditions:
if so, rejecting data corresponding to time periods in which an unreasonable first average vehicle speed, flow rate, or density exists.
Based on the above embodiment, the preset determination condition includes:
judging whether the first average vehicle speed, the flow rate or the density corresponding to any one time period is smaller than a first preset value: if so, indicating that the data in the time interval is unreasonable;
otherwise, continuously judging whether the flow rate is greater than the first preset value while the first average vehicle speed is the first preset value:
if so, indicating that the data in the time interval is unreasonable; otherwise, continuously judging whether the first average vehicle speed is greater than a second preset value:
if so, indicating that the data in the time interval is unreasonable;
otherwise, the data representing the time period is reasonable.
Based on the above embodiment, the second obtaining module 03 includes:
a first acquisition unit 031 acquires a traffic flow basic diagram to be evaluated and a general formula of a linear converter;
and a construction unit 032, combining the general form of the linear converter with the traffic flow basic diagram to obtain a linear converter corresponding to the traffic flow basic diagram.
Based on the above embodiment, the calculation module 05 includes:
the sorting unit 051 sorts the densities corresponding to all the time periods according to an ascending order;
a fourth calculating unit 052, calculating the weight corresponding to each time interval according to the sorted density;
a fifth calculating unit 053, calculating to obtain a first average vehicle speed mean value after coordinate conversion according to the first average vehicle speed after coordinate conversion;
and a sixth calculating unit 054, calculating to obtain a weighted decision coefficient of the traffic flow according to the density corresponding to all the time intervals, the first average vehicle speed after coordinate conversion, the first average vehicle speed average value and the weight.
It should be noted that, regarding the system in the above embodiment, the specific manner in which each module performs the operation has been described in detail in the embodiment related to the method, and will not be elaborated herein.
Example 3
Corresponding to the above method embodiment, the present embodiment further provides a device for evaluating the fitting performance of the traffic flow basic diagram, and the device for evaluating the fitting performance of the traffic flow basic diagram described below and the method for evaluating the fitting performance of the traffic flow basic diagram described above may be referred to correspondingly.
Fig. 3 is a block diagram illustrating a fitting performance evaluation apparatus 800 of a traffic flow basic diagram according to an exemplary embodiment. As shown in fig. 3, the fitting performance evaluation apparatus 800 of the traffic flow basic diagram may include: a processor 801, a memory 802. The fitting performance evaluation device 800 of the traffic flow basic map may further include one or more of a multimedia component 803, an I/O interface 804, and a communication component 805.
The processor 801 is configured to control the overall operation of the fitting performance evaluation apparatus 800 of the traffic flow basic diagram, so as to complete all or part of the steps in the method for evaluating the fitting performance of the traffic flow basic diagram. The memory 802 is used to store various types of data to support the operation of the traffic flow basic diagram fitting performance evaluation device 800, which may include, for example, instructions for any application or method operating on the traffic flow basic diagram fitting performance evaluation device 800, as well as application-related data, such as contact data, transceived messages, pictures, audio, video, and the like. The Memory 802 may be implemented by any type of volatile or non-volatile Memory device or combination thereof, such as Static Random Access Memory (SRAM), electrically Erasable Programmable Read-Only Memory (EEPROM), erasable Programmable Read-Only Memory (EPROM), programmable Read-Only Memory (PROM), read-Only Memory (ROM), magnetic Memory, flash Memory, magnetic disk or optical disk. The multimedia components 803 may include screen and audio components. Wherein the screen may be, for example, a touch screen and the audio component is used for outputting and/or inputting audio signals. For example, the audio component may include a microphone for receiving external audio signals. The received audio signal may further be stored in the memory 802 or transmitted through the communication component 805. The audio assembly also includes at least one speaker for outputting audio signals. The I/O interface 804 provides an interface between the processor 801 and other interface modules, such as a keyboard, mouse, buttons, etc. These buttons may be virtual buttons or physical buttons. The communication component 805 is used for performing wired or wireless communication between the fitting performance evaluation device 800 of the traffic flow basic diagram and other devices. Wireless communication, such as Wi-Fi, bluetooth, near Field Communication (NFC), 2G, 3G, or 4G, or a combination of one or more of them, so that the corresponding communication component 805 may include: wi-Fi module, bluetooth module, NFC module.
In an exemplary embodiment, the fitting performance evaluation Device 800 of the traffic flow basic diagram may be implemented by one or more Application Specific Integrated Circuits (ASICs), digital Signal Processors (DSPs), digital Signal Processing Devices (DSPDs), programmable Logic Devices (PLDs), field Programmable Gate Arrays (FPGAs), controllers, microcontrollers, microprocessors or other electronic components, for performing the fitting performance evaluation method of the traffic flow basic diagram.
In another exemplary embodiment, there is also provided a computer-readable storage medium including program instructions which, when executed by a processor, implement the steps of the above-described method for evaluating the fitting performance of a traffic flow basic map. For example, the computer-readable storage medium may be the above-described memory 802 including program instructions executable by the processor 801 of the traffic flow basic map fitting performance evaluation apparatus 800 to perform the above-described traffic flow basic map fitting performance evaluation method.
Example 4
Corresponding to the above method embodiment, a readable storage medium is also provided in this embodiment, and a readable storage medium described below and the above method for evaluating the fitting performance of the traffic flow basic diagram may be referred to correspondingly.
A readable storage medium, on which a computer program is stored, the computer program, when executed by a processor, implementing the steps of the method for evaluating the fitting performance of the traffic flow basic diagram according to the above-mentioned method embodiment.
The readable storage medium may be a usb disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and various other readable storage media capable of storing program codes.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
The above description is only for the specific embodiments of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present invention, and all the changes or substitutions should be covered within the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (10)
1. A method for evaluating the fitting performance of a traffic flow basic diagram is characterized by comprising the following steps:
acquiring vehicle count and time average speed of a traffic flow in a preset period;
dividing the preset period into a plurality of time periods, and preprocessing the vehicle count and the time average speed to obtain a first average vehicle speed and a first average density corresponding to the traffic flow in each time period;
obtaining a traffic flow basic diagram to be evaluated, and constructing a linear converter corresponding to the traffic flow basic diagram;
performing coordinate transformation on the first average vehicle speed by using the linear converter;
calculating to obtain a weighting decision coefficient of the traffic flow in the preset period based on the density of all the time periods and the first average vehicle speed after coordinate conversion;
and evaluating the fitting performance of the traffic flow basic diagram to the traffic flow according to the weighting decision coefficient.
2. The method for evaluating the fitting performance of the traffic flow basic map according to claim 1, wherein the preprocessing of the vehicle count and the time-average speed is performed to obtain a first average vehicle speed and density corresponding to the traffic flow in each period, and comprises:
calculating the flow rate corresponding to the traffic flow in each time interval according to the vehicle count;
calculating to obtain a first average vehicle speed corresponding to the traffic flow in each time period according to the time average speed;
calculating to obtain the density corresponding to the traffic flow in each time period by using the first average vehicle speed and the flow rate corresponding to the traffic flow in each time period;
judging whether an unreasonable first average vehicle speed, flow rate or density exists in each time period according to a preset judgment condition:
if so, rejecting data corresponding to time periods in which an unreasonable first average vehicle speed, flow rate, or density exists.
3. The method for evaluating the fitting performance of the traffic flow basic diagram according to claim 2, wherein the preset judgment condition includes:
judging whether the first average vehicle speed, the flow rate or the density corresponding to any one time period is smaller than a first preset value: if so, indicating that the data in the time interval is unreasonable;
otherwise, continuously judging whether the flow rate is larger than the first preset value while the first average vehicle speed is the first preset value:
if so, indicating that the data in the time interval is unreasonable; otherwise, continuously judging whether the first average vehicle speed is greater than a second preset value:
if yes, indicating that the data in the time interval is unreasonable;
otherwise, the data representing the time period is reasonable.
4. The method for evaluating the fitting performance of the traffic flow basic diagram according to claim 1, wherein the acquiring of the traffic flow basic diagram to be evaluated and the construction of the linearized converter corresponding to the traffic flow basic diagram comprise:
acquiring a traffic flow basic diagram to be evaluated and a general formula of a linear converter;
and combining the general formula of the linearized converter with the traffic flow basic diagram to obtain the linearized converter corresponding to the traffic flow basic diagram.
5. The method for evaluating the fitting performance of the traffic flow basic diagram according to claim 1, wherein a weighting decision coefficient of the traffic flow in the preset period is calculated based on the density of all the periods and the first average vehicle speed after the coordinate conversion, and the method comprises the following steps:
arranging the densities corresponding to all the time periods in an ascending order;
calculating to obtain the weight corresponding to each time interval according to the sorted density;
calculating to obtain a first average vehicle speed mean value after coordinate conversion according to the first average vehicle speed after coordinate conversion;
and calculating the weighted decision coefficient of the traffic flow according to the density corresponding to all time intervals, the first average vehicle speed after coordinate conversion, the first average vehicle speed average value and the weight.
6. A fitting performance evaluation system of a traffic flow basic diagram is characterized by comprising:
a first obtaining module: acquiring vehicle count and time average speed of a traffic flow in a preset period;
a processing module: dividing the preset period into a plurality of time intervals, and preprocessing the vehicle count and the time average speed to obtain a first average vehicle speed and a first average density corresponding to the traffic flow in each time interval;
a second obtaining module: obtaining a traffic flow basic diagram to be evaluated, and constructing a linear converter corresponding to the traffic flow basic diagram;
a coordinate conversion module: performing coordinate transformation on the first average vehicle speed by using the linear converter;
a calculation module: calculating to obtain a weighting decision coefficient of the traffic flow in the preset period based on the density of all the time periods and the first average vehicle speed after coordinate conversion;
an evaluation module: and evaluating the fitting performance of the traffic flow basic diagram to the traffic flow according to the weighting decision coefficient.
7. The fit performance evaluation system of the traffic flow basic map according to claim 6, wherein the processing module includes:
the first calculation unit: calculating the flow rate corresponding to the traffic flow in each time interval according to the vehicle count;
a second calculation unit: calculating to obtain a first average speed corresponding to the traffic flow in each time interval according to the time average speed;
a third calculation unit: calculating to obtain the density corresponding to the traffic flow in each time period by using the first average vehicle speed and the flow rate corresponding to the traffic flow in each time period;
a judging unit: judging whether an unreasonable first average vehicle speed, flow rate or density exists in each time interval according to preset judgment conditions:
and if so, rejecting data corresponding to the time period in which the unreasonable first average vehicle speed, flow rate or density exists.
8. The fitting performance evaluation system of the traffic flow basic map according to claim 7, characterized in that the judgment unit includes:
judging whether the first average vehicle speed, the flow rate or the density corresponding to any one time period is smaller than a first preset value: if yes, indicating that the data in the time interval is unreasonable;
otherwise, continuously judging whether the flow rate is greater than the first preset value while the first average vehicle speed is the first preset value:
if so, indicating that the data in the time interval is unreasonable; otherwise, continuously judging whether the first average vehicle speed is greater than a second preset value:
if so, indicating that the data in the time interval is unreasonable;
otherwise, the data representing the time period is reasonable.
9. The fitting performance evaluation system of the traffic flow basic map according to claim 6, wherein the second acquisition module includes:
a first acquisition unit: acquiring a traffic flow basic diagram to be evaluated and a general formula of a linear converter;
a construction unit: and combining the general formula of the linear converter with the traffic flow basic diagram to obtain the linear converter corresponding to the traffic flow basic diagram.
10. The fit performance evaluation system of the traffic flow basic map according to claim 6, wherein the calculation module includes:
a sorting unit: arranging the densities corresponding to all the time periods in an ascending order;
a fourth calculation unit: calculating to obtain the weight corresponding to each time interval according to the sorted density;
a fifth calculation unit: calculating to obtain a first average vehicle speed mean value after coordinate conversion according to the first average vehicle speed after coordinate conversion;
a sixth calculation unit: and calculating the weighted decision coefficient of the traffic flow according to the density corresponding to all time intervals, the first average vehicle speed after coordinate conversion, the first average vehicle speed average value and the weight.
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Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20150120174A1 (en) * | 2013-10-31 | 2015-04-30 | Here Global B.V. | Traffic Volume Estimation |
| CN105825683A (en) * | 2015-08-16 | 2016-08-03 | 李萌 | Road traffic condition evaluation method |
| US9633560B1 (en) * | 2016-03-30 | 2017-04-25 | Jason Hao Gao | Traffic prediction and control system for vehicle traffic flows at traffic intersections |
| CN111127880A (en) * | 2019-12-16 | 2020-05-08 | 西南交通大学 | MFD-based grid network traffic performance analysis method |
| CN112201037A (en) * | 2020-09-27 | 2021-01-08 | 同济大学 | Intersection arrival rate estimation method based on sampling trajectory data |
| CN113947905A (en) * | 2021-10-19 | 2022-01-18 | 交通运输部公路科学研究所 | Traffic operation situation sensing method, module and system |
| CN114155717A (en) * | 2022-02-10 | 2022-03-08 | 西南交通大学 | Traffic flow data screening method, device, equipment and readable storage medium |
| EP4030376A1 (en) * | 2021-01-16 | 2022-07-20 | Bayerische Motoren Werke Aktiengesellschaft | Method for determining an impact of ride pooling on a traffic situation |
| CN114783183A (en) * | 2022-04-15 | 2022-07-22 | 中远海运科技股份有限公司 | Monitoring method and system based on traffic situation algorithm |
| CN115457768A (en) * | 2022-08-30 | 2022-12-09 | 北京理工大学 | Dynamics analysis method of traffic flow model considering relevant random speed conversion rate |
-
2023
- 2023-02-10 CN CN202310096992.1A patent/CN115830874B/en active Active
Patent Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20150120174A1 (en) * | 2013-10-31 | 2015-04-30 | Here Global B.V. | Traffic Volume Estimation |
| CN105825683A (en) * | 2015-08-16 | 2016-08-03 | 李萌 | Road traffic condition evaluation method |
| US9633560B1 (en) * | 2016-03-30 | 2017-04-25 | Jason Hao Gao | Traffic prediction and control system for vehicle traffic flows at traffic intersections |
| CN111127880A (en) * | 2019-12-16 | 2020-05-08 | 西南交通大学 | MFD-based grid network traffic performance analysis method |
| CN112201037A (en) * | 2020-09-27 | 2021-01-08 | 同济大学 | Intersection arrival rate estimation method based on sampling trajectory data |
| EP4030376A1 (en) * | 2021-01-16 | 2022-07-20 | Bayerische Motoren Werke Aktiengesellschaft | Method for determining an impact of ride pooling on a traffic situation |
| CN113947905A (en) * | 2021-10-19 | 2022-01-18 | 交通运输部公路科学研究所 | Traffic operation situation sensing method, module and system |
| CN114155717A (en) * | 2022-02-10 | 2022-03-08 | 西南交通大学 | Traffic flow data screening method, device, equipment and readable storage medium |
| CN114783183A (en) * | 2022-04-15 | 2022-07-22 | 中远海运科技股份有限公司 | Monitoring method and system based on traffic situation algorithm |
| CN115457768A (en) * | 2022-08-30 | 2022-12-09 | 北京理工大学 | Dynamics analysis method of traffic flow model considering relevant random speed conversion rate |
Non-Patent Citations (10)
| Title |
|---|
| FEIFEI XU等: "Survey the impact of different rainfall intensities on urban road traffic operations using Macroscopic Fundamental Diagram" * |
| JING LIU等: "Dynamic Traffic Flow Prediction Based on Long-Short Term Memory Framework With Feature Organization" * |
| LU BAI等: "Calibration of stochastic link-based fundamental diagram with explicit consideration of speed heterogeneity" * |
| 刘澜: "面向边界控制的路网小区划分", 中国公路学报 * |
| 张南等: "城市干路宏观基本图稳定性研究" * |
| 杨昀霖;何兆成;王亦民;: "基于过车速度分布的中观仿真模型参数校准" * |
| 林云志: "基于车牌识别数据的宏观基本图研究" * |
| 王福建: "基于宏观基本图的城市路网交通状态判别与监控" * |
| 葛梦雪: "基于多维特征聚类的交通量模式识别与测算方法研究" * |
| 邵长桥: "宏观交通流模型参数标定方法" * |
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